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Record W4380677092 · doi:10.3899/jrheum.2022-1277

The Effect of COVID-19 on Medication Adherence in a Rheumatoid Arthritis (BRAGGSS) and Psoriatic Arthritis (OUTPASS) UK Cohort

2023· letter· en· W4380677092 on OpenAlexvenueno aff
Philippa D. K. Curry, Hector Chinoy, Meghna Jani, Darren Plant, Kimmie L. Hyrich, Ann W Morgan, Anthony G. Wilson, John D. Isaacs, Andrew P. Morris, Anne Barton, James Bluett

Bibliographic record

VenueThe Journal of Rheumatology · 2023
Typeletter
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
FundersManchester Biomedical Research CentreMedical Research CouncilVersus ArthritisChugai PharmaceuticalNewcastle upon Tyne Hospitals NHS Foundation TrustNewcastle UniversityDepartment of Health and Social CareUniversity of LeedsNational Institute for Health and Care ResearchRegeneron PharmaceuticalsGilead SciencesSanofiPfizerKiniksa PharmaceuticalsAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineRheumatoid arthritisPsoriatic arthritisCoronavirus disease 2019 (COVID-19)CohortPandemicAnxietyInternal medicineArthritisDiseaseDermatologyPsychiatryInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Suboptimal treatment adherence has been reported in patients with arthritic diseases; is associated with psychological factors, including anxiety; and correlates with future treatment response.1,2 During the coronavirus disease 2019 (COVID-19) pandemic, patients who identified as clinically extremely vulnerable, including people prescribed ≥ 2 immunosuppressives, were advised to shield and continue treatment unless they developed COVID-19 symptoms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.289
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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